Benefits of Machine Learning for Big Data Analytics

Machine learning is an advanced data analysis that enables automated analytical model building. Machine learning algorithms continuously assess and keep ‘learning’ from available data. It is more like empowering a computer with certain resources and giving a target. This computer now goes through a series of permutation and combination with all the data in hand, to achieve that final target. In the process, it starts learning and adapting the best possible technique to achieve that particular target. In this case, no external codes are written. The computer learns through real-time data analysis.

Machine learning reduces the work of computer coders by half. Imagine the series of codes a coder would have had to write had it been a manual process where codes have to be input for a computer to analyze and predict outcomes from huge sets of data.

Machine learning is the one the efficient ways of handling data, predicting the outcomes, and be on-time.

The advantages of machine learning in big data analytics are many. Here are few of the most important ones.

Benefits of machine learning in big data analytics

#1 Unlimited data analysis in a shorter time span

Machine learning can handle an unlimited amount of data, assess them and provide a proper analysis for the same. This factor enables businesses to target customers with relevant messages that are based on customer activities and interactions. Machine learning can pinpoint variously related variables once it has forged a model from multiple sources of data. This helps in avoiding integration complications and makes way for more precise data results.

#2 Real-time data prediction for analysts to research

Big data analysts are looking at machine learning as the most effective source for precise data prediction. It consumes a humongous amount of data, thoroughly goes over all the related trends and activities, and finally provides concise and precise forecasts with real-time data. This exact nature of data empowers analysts to dig in further into their research, understand data better and utilize it for the betterment of multiple industries. One way of explaining it is: machine learning can precisely optimize new grocery offers for departmental customers at different time slots. This means, what the customers will see at 3 PM is different from what they saw at 1 PM.

How is machine learning helping big data analysts?

Most businesses are employing big data analysts to push their revenue game. In turn, big data analysts are relying on machine learning to do this work for them.

Machine learning uses churn analysis to pinpoint those customers that will leave.

It puts into play customer leads, conversions, and revenue rates, buying patterns and spending history.

It uses the same technology to go over customer defections towards competitor brands.

How this benefits the data analyst?

Churn analysis helps in identifying the loopholes that might have driven away potential leads. Machine learning detects the potential leads that will opt out soon. Using this data, analysts can dig deeper to understand “why” and “what can be done.”

By analyzing buying patterns, revenue history, etc. analysts can strategize their customers based on priority and accordingly use their resources to engage with them.

Data analysis dealing with customer defections can help analysts in keeping a retention plan handy so that the situation comes under control even before the mess starts.

Machine learning has numerous advantages in big data analysis, the foremost being its ability to learn on its own, analyze data sets and provide precise insights that are valuable for analysts.

It is proactive and is mainly designed for industries that go by the “actions and reactions” formulae. It becomes easier for systems to pick up actions based on predictions made by machine learning. Since these predictions are immensely accurate, machine learning is a boon for any business or industry. It is the key to propelling business based on real facts and industry-specific predictions.

4 thoughts on “Benefits of Machine Learning for Big Data Analytics”

The simple answer to your question is, whichever field interests you. Although Machine learning and big data fall under the umbrella term data science there is some difference between the two. I will try explaining both the terms but you must choose the one which interests you.

I too agree with the Andrew ng’s machine learning course its fascinating but it gives you a basic flavor of Machine Learning,but if you look the industries problems these courses are very far behind. So I believe Andrew ng’s course is only not the way to get a ML job you need a domain knowledge along with it. On the second part people are running after Big Data, majorly there are basically three types of role on the top of Big data i.e Data Engineer, Big data Architects and Data Scientist. The main role of data engineer is the data retrieval uses parallel queries and other stuffs finally provide data to the scientist for running ML on it. Big Data Architect is also a good area because they are ones who decides the number of clusters, nodes to be setup for end to end system. So the final decision is on your goal.

Machine Learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this course is to give you in-depth knowledge of the various data analytics techniques that can be performed using R and various statistical concepts. This course is packed with multiple real-time projects and hands-on exercises.